Overview

Dataset statistics

Number of variables13
Number of observations4765
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory502.6 KiB
Average record size in memory108.0 B

Variable types

DateTime1
Categorical3
Numeric8
Unsupported1

Alerts

location has a high cardinality: 3930 distinct valuesHigh cardinality
operator has a high cardinality: 2201 distinct valuesHigh cardinality
ac_type has a high cardinality: 2370 distinct valuesHigh cardinality
passenger_aboard is highly overall correlated with passenger_fatalitiesHigh correlation
crew_aboard is highly overall correlated with crew_fatalitiesHigh correlation
passenger_fatalities is highly overall correlated with passenger_aboardHigh correlation
crew_fatalities is highly overall correlated with crew_aboardHigh correlation
decade is highly overall correlated with yearHigh correlation
year is highly overall correlated with decadeHigh correlation
location is uniformly distributedUniform
ground is an unsupported type, check if it needs cleaning or further analysisUnsupported
passenger_aboard has 869 (18.2%) zerosZeros
all_fatalities has 74 (1.6%) zerosZeros
passenger_fatalities has 1039 (21.8%) zerosZeros
crew_fatalities has 398 (8.4%) zerosZeros

Reproduction

Analysis started2023-05-24 08:12:23.706989
Analysis finished2023-05-24 08:13:19.625437
Duration55.92 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Distinct4366
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size74.5 KiB
Minimum1908-09-17 00:00:00
Maximum2021-07-06 00:00:00
2023-05-24T05:13:19.783443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:19.989502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

location
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3930
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size74.5 KiB
Manila, Philippines
 
15
Moscow, Russia
 
15
New York, New York
 
14
Rio de Janeiro, Brazil
 
12
Cairo, Egypt
 
12
Other values (3925)
4697 

Length

Max length72
Median length51
Mean length20.823924
Min length1

Characters and Unicode

Total characters99226
Distinct characters90
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3514 ?
Unique (%)73.7%

Sample

1st rowFort Myer, Virginia
2nd rowJuvisy-sur-Orge, France
3rd rowAtlantic City, New Jersey
4th rowVictoria, British Columbia, Canada
5th rowTienen, Belgium

Common Values

ValueCountFrequency (%)
Manila, Philippines 15
 
0.3%
Moscow, Russia 15
 
0.3%
New York, New York 14
 
0.3%
Rio de Janeiro, Brazil 12
 
0.3%
Cairo, Egypt 12
 
0.3%
Sao Paulo, Brazil 12
 
0.3%
Bogota, Colombia 12
 
0.3%
Chicago, Illinois 10
 
0.2%
Near Moscow, Russia 10
 
0.2%
Tehran, Iran 9
 
0.2%
Other values (3920) 4644
97.5%

Length

2023-05-24T05:13:20.307215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
near 1279
 
9.2%
off 319
 
2.3%
russia 250
 
1.8%
new 224
 
1.6%
brazil 169
 
1.2%
colombia 153
 
1.1%
canada 127
 
0.9%
france 118
 
0.8%
california 115
 
0.8%
mexico 110
 
0.8%
Other values (3994) 11097
79.5%

Most occurring characters

ValueCountFrequency (%)
a 12410
 
12.5%
9244
 
9.3%
e 6733
 
6.8%
i 6301
 
6.4%
n 6205
 
6.3%
r 5734
 
5.8%
o 5150
 
5.2%
, 4985
 
5.0%
l 3826
 
3.9%
s 3393
 
3.4%
Other values (80) 35245
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70598
71.1%
Uppercase Letter 14041
 
14.2%
Space Separator 9245
 
9.3%
Other Punctuation 5136
 
5.2%
Dash Punctuation 98
 
0.1%
Decimal Number 66
 
0.1%
Control 20
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12410
17.6%
e 6733
9.5%
i 6301
8.9%
n 6205
8.8%
r 5734
 
8.1%
o 5150
 
7.3%
l 3826
 
5.4%
s 3393
 
4.8%
t 2949
 
4.2%
u 2629
 
3.7%
Other values (31) 15268
21.6%
Uppercase Letter
ValueCountFrequency (%)
N 1932
13.8%
C 1405
 
10.0%
S 1073
 
7.6%
M 961
 
6.8%
B 909
 
6.5%
A 861
 
6.1%
P 760
 
5.4%
I 687
 
4.9%
R 638
 
4.5%
O 541
 
3.9%
Other values (17) 4274
30.4%
Decimal Number
ValueCountFrequency (%)
0 24
36.4%
1 15
22.7%
2 9
 
13.6%
5 8
 
12.1%
8 3
 
4.5%
9 2
 
3.0%
3 2
 
3.0%
7 2
 
3.0%
6 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 4985
97.1%
. 115
 
2.2%
' 24
 
0.5%
/ 6
 
0.1%
? 5
 
0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9244
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
15
75.0%
5
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84639
85.3%
Common 14587
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12410
14.7%
e 6733
 
8.0%
i 6301
 
7.4%
n 6205
 
7.3%
r 5734
 
6.8%
o 5150
 
6.1%
l 3826
 
4.5%
s 3393
 
4.0%
t 2949
 
3.5%
u 2629
 
3.1%
Other values (58) 29309
34.6%
Common
ValueCountFrequency (%)
9244
63.4%
, 4985
34.2%
. 115
 
0.8%
- 98
 
0.7%
0 24
 
0.2%
' 24
 
0.2%
15
 
0.1%
1 15
 
0.1%
) 11
 
0.1%
( 11
 
0.1%
Other values (12) 45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99186
> 99.9%
None 40
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12410
 
12.5%
9244
 
9.3%
e 6733
 
6.8%
i 6301
 
6.4%
n 6205
 
6.3%
r 5734
 
5.8%
o 5150
 
5.2%
, 4985
 
5.0%
l 3826
 
3.9%
s 3393
 
3.4%
Other values (63) 35205
35.5%
None
ValueCountFrequency (%)
é 12
30.0%
ö 5
12.5%
ó 4
 
10.0%
í 4
 
10.0%
ï 2
 
5.0%
á 2
 
5.0%
è 1
 
2.5%
ô 1
 
2.5%
à 1
 
2.5%
ä 1
 
2.5%
Other values (7) 7
17.5%

operator
Categorical

Distinct2201
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Memory size74.5 KiB
Aeroflot
 
247
Military - U.S. Air Force
 
132
Air France
 
65
Deutsche Lufthansa
 
63
United Air Lines
 
44
Other values (2196)
4214 

Length

Max length65
Median length48
Mean length18.701364
Min length1

Characters and Unicode

Total characters89112
Distinct characters87
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1695 ?
Unique (%)35.6%

Sample

1st rowMilitary - U.S. Army
2nd row?
3rd rowMilitary - U.S. Navy
4th rowPrivate
5th rowMilitary - German Navy

Common Values

ValueCountFrequency (%)
Aeroflot 247
 
5.2%
Military - U.S. Air Force 132
 
2.8%
Air France 65
 
1.4%
Deutsche Lufthansa 63
 
1.3%
United Air Lines 44
 
0.9%
Military - U.S. Army Air Forces 43
 
0.9%
Pan American World Airways 40
 
0.8%
China National Aviation Corporation 37
 
0.8%
American Airlines 36
 
0.8%
US Aerial Mail Service 34
 
0.7%
Other values (2191) 4024
84.4%

Length

2023-05-24T05:13:20.547205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
air 1371
 
10.2%
airlines 831
 
6.2%
823
 
6.1%
military 638
 
4.7%
force 476
 
3.5%
airways 437
 
3.2%
u.s 263
 
2.0%
aeroflot 259
 
1.9%
lines 183
 
1.4%
aviation 136
 
1.0%
Other values (2039) 8037
59.7%

Most occurring characters

ValueCountFrequency (%)
i 9582
 
10.8%
8709
 
9.8%
r 8276
 
9.3%
a 7317
 
8.2%
e 6494
 
7.3%
n 5312
 
6.0%
A 4842
 
5.4%
o 4142
 
4.6%
s 3878
 
4.4%
l 3826
 
4.3%
Other values (77) 26734
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64380
72.2%
Uppercase Letter 14184
 
15.9%
Space Separator 8710
 
9.8%
Dash Punctuation 793
 
0.9%
Other Punctuation 785
 
0.9%
Open Punctuation 113
 
0.1%
Close Punctuation 113
 
0.1%
Decimal Number 26
 
< 0.1%
Control 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9582
14.9%
r 8276
12.9%
a 7317
11.4%
e 6494
10.1%
n 5312
8.3%
o 4142
6.4%
s 3878
 
6.0%
l 3826
 
5.9%
t 3688
 
5.7%
c 1865
 
2.9%
Other values (28) 10000
15.5%
Uppercase Letter
ValueCountFrequency (%)
A 4842
34.1%
S 1071
 
7.6%
M 1057
 
7.5%
C 870
 
6.1%
F 805
 
5.7%
T 662
 
4.7%
L 652
 
4.6%
P 494
 
3.5%
U 485
 
3.4%
N 456
 
3.2%
Other values (16) 2790
19.7%
Decimal Number
ValueCountFrequency (%)
0 5
19.2%
7 4
15.4%
4 4
15.4%
8 2
 
7.7%
6 2
 
7.7%
9 2
 
7.7%
5 2
 
7.7%
2 2
 
7.7%
1 2
 
7.7%
3 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 639
81.4%
/ 99
 
12.6%
' 23
 
2.9%
, 10
 
1.3%
? 8
 
1.0%
& 6
 
0.8%
Space Separator
ValueCountFrequency (%)
8709
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
6
75.0%
2
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 793
100.0%
Open Punctuation
ValueCountFrequency (%)
( 113
100.0%
Close Punctuation
ValueCountFrequency (%)
) 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78564
88.2%
Common 10548
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9582
12.2%
r 8276
 
10.5%
a 7317
 
9.3%
e 6494
 
8.3%
n 5312
 
6.8%
A 4842
 
6.2%
o 4142
 
5.3%
s 3878
 
4.9%
l 3826
 
4.9%
t 3688
 
4.7%
Other values (54) 21207
27.0%
Common
ValueCountFrequency (%)
8709
82.6%
- 793
 
7.5%
. 639
 
6.1%
( 113
 
1.1%
) 113
 
1.1%
/ 99
 
0.9%
' 23
 
0.2%
, 10
 
0.1%
? 8
 
0.1%
6
 
0.1%
Other values (13) 35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88993
99.9%
None 119
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9582
 
10.8%
8709
 
9.8%
r 8276
 
9.3%
a 7317
 
8.2%
e 6494
 
7.3%
n 5312
 
6.0%
A 4842
 
5.4%
o 4142
 
4.7%
s 3878
 
4.4%
l 3826
 
4.3%
Other values (64) 26615
29.9%
None
ValueCountFrequency (%)
é 99
83.2%
á 5
 
4.2%
à 2
 
1.7%
í 2
 
1.7%
ï 2
 
1.7%
ó 2
 
1.7%
ú 1
 
0.8%
  1
 
0.8%
ã 1
 
0.8%
è 1
 
0.8%
Other values (3) 3
 
2.5%

ac_type
Categorical

Distinct2370
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size74.5 KiB
Douglas DC-3
 
314
de Havilland Canada DHC-6 Twin Otter 300
 
81
Douglas C-47A
 
69
Douglas C-47
 
57
Douglas DC-4
 
40
Other values (2365)
4204 

Length

Max length42
Median length36
Mean length18.528856
Min length1

Characters and Unicode

Total characters88290
Distinct characters76
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1784 ?
Unique (%)37.4%

Sample

1st rowWright Flyer III
2nd rowWright Byplane
3rd rowDirigible
4th rowCurtiss seaplane
5th rowZeppelin L-8 (airship)

Common Values

ValueCountFrequency (%)
Douglas DC-3 314
 
6.6%
de Havilland Canada DHC-6 Twin Otter 300 81
 
1.7%
Douglas C-47A 69
 
1.4%
Douglas C-47 57
 
1.2%
Douglas DC-4 40
 
0.8%
Yakovlev YAK-40 35
 
0.7%
Antonov AN-26 31
 
0.7%
Junkers JU-52/3m 29
 
0.6%
De Havilland DH-4 27
 
0.6%
Douglas DC-6B 27
 
0.6%
Other values (2360) 4055
85.1%

Length

2023-05-24T05:13:20.899285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
douglas 1085
 
8.4%
boeing 402
 
3.1%
dc-3 367
 
2.8%
lockheed 315
 
2.4%
de 294
 
2.3%
havilland 292
 
2.3%
antonov 273
 
2.1%
canada 159
 
1.2%
otter 146
 
1.1%
ilyushin 140
 
1.1%
Other values (2442) 9494
73.2%

Most occurring characters

ValueCountFrequency (%)
8231
 
9.3%
- 4960
 
5.6%
e 4597
 
5.2%
a 4474
 
5.1%
o 4394
 
5.0%
n 3716
 
4.2%
l 3489
 
4.0%
i 3223
 
3.7%
r 3132
 
3.5%
C 2927
 
3.3%
Other values (66) 45147
51.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44080
49.9%
Uppercase Letter 17142
 
19.4%
Decimal Number 13340
 
15.1%
Space Separator 8232
 
9.3%
Dash Punctuation 4960
 
5.6%
Other Punctuation 240
 
0.3%
Open Punctuation 146
 
0.2%
Close Punctuation 145
 
0.2%
Math Symbol 3
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4597
10.4%
a 4474
10.1%
o 4394
10.0%
n 3716
 
8.4%
l 3489
 
7.9%
i 3223
 
7.3%
r 3132
 
7.1%
s 2765
 
6.3%
t 2241
 
5.1%
u 2129
 
4.8%
Other values (18) 9920
22.5%
Uppercase Letter
ValueCountFrequency (%)
C 2927
17.1%
D 2728
15.9%
A 1834
10.7%
B 1657
9.7%
H 956
 
5.6%
L 823
 
4.8%
F 778
 
4.5%
S 728
 
4.2%
I 628
 
3.7%
T 606
 
3.5%
Other values (16) 3477
20.3%
Decimal Number
ValueCountFrequency (%)
2 2092
15.7%
0 2054
15.4%
1 1945
14.6%
4 1646
12.3%
3 1625
12.2%
7 1449
10.9%
6 849
6.4%
5 688
 
5.2%
8 635
 
4.8%
9 357
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/ 163
67.9%
. 70
29.2%
? 4
 
1.7%
, 2
 
0.8%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
8231
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4960
100.0%
Open Punctuation
ValueCountFrequency (%)
( 146
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61222
69.3%
Common 27068
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4597
 
7.5%
a 4474
 
7.3%
o 4394
 
7.2%
n 3716
 
6.1%
l 3489
 
5.7%
i 3223
 
5.3%
r 3132
 
5.1%
C 2927
 
4.8%
s 2765
 
4.5%
D 2728
 
4.5%
Other values (44) 25777
42.1%
Common
ValueCountFrequency (%)
8231
30.4%
- 4960
18.3%
2 2092
 
7.7%
0 2054
 
7.6%
1 1945
 
7.2%
4 1646
 
6.1%
3 1625
 
6.0%
7 1449
 
5.4%
6 849
 
3.1%
5 688
 
2.5%
Other values (12) 1529
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88275
> 99.9%
None 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8231
 
9.3%
- 4960
 
5.6%
e 4597
 
5.2%
a 4474
 
5.1%
o 4394
 
5.0%
n 3716
 
4.2%
l 3489
 
4.0%
i 3223
 
3.7%
r 3132
 
3.5%
C 2927
 
3.3%
Other values (63) 45132
51.1%
None
ValueCountFrequency (%)
é 11
73.3%
è 3
 
20.0%
  1
 
6.7%

all_aboard
Real number (ℝ)

Distinct243
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.364953
Minimum0
Maximum644
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:21.107380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median16
Q335
95-th percentile119
Maximum644
Range644
Interquartile range (IQR)29

Descriptive statistics

Standard deviation46.131898
Coefficient of variation (CV)1.4708104
Kurtosis23.407024
Mean31.364953
Median Absolute Deviation (MAD)11
Skewness3.8770721
Sum149454
Variance2128.152
MonotonicityNot monotonic
2023-05-24T05:13:21.264965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 278
 
5.8%
2 239
 
5.0%
4 199
 
4.2%
5 187
 
3.9%
6 172
 
3.6%
10 171
 
3.6%
7 162
 
3.4%
1 137
 
2.9%
9 123
 
2.6%
11 118
 
2.5%
Other values (233) 2979
62.5%
ValueCountFrequency (%)
0 5
 
0.1%
1 137
2.9%
2 239
5.0%
3 278
5.8%
4 199
4.2%
5 187
3.9%
6 172
3.6%
7 162
3.4%
8 117
2.5%
9 123
2.6%
ValueCountFrequency (%)
644 1
< 0.1%
524 1
< 0.1%
517 1
< 0.1%
394 1
< 0.1%
393 1
< 0.1%
384 1
< 0.1%
356 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
340 1
< 0.1%

passenger_aboard
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct234
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.860021
Minimum0
Maximum614
Zeros869
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:21.449973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q330
95-th percentile112
Maximum614
Range614
Interquartile range (IQR)27

Descriptive statistics

Standard deviation44.099535
Coefficient of variation (CV)1.641828
Kurtosis24.155408
Mean26.860021
Median Absolute Deviation (MAD)11
Skewness3.9368972
Sum127988
Variance1944.769
MonotonicityNot monotonic
2023-05-24T05:13:21.658386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 869
 
18.2%
4 170
 
3.6%
2 161
 
3.4%
5 140
 
2.9%
3 129
 
2.7%
7 129
 
2.7%
9 128
 
2.7%
10 127
 
2.7%
8 124
 
2.6%
1 120
 
2.5%
Other values (224) 2668
56.0%
ValueCountFrequency (%)
0 869
18.2%
1 120
 
2.5%
2 161
 
3.4%
3 129
 
2.7%
4 170
 
3.6%
5 140
 
2.9%
6 109
 
2.3%
7 129
 
2.7%
8 124
 
2.6%
9 128
 
2.7%
ValueCountFrequency (%)
614 1
< 0.1%
509 1
< 0.1%
503 1
< 0.1%
381 1
< 0.1%
374 1
< 0.1%
364 1
< 0.1%
338 1
< 0.1%
335 1
< 0.1%
327 1
< 0.1%
316 1
< 0.1%

crew_aboard
Real number (ℝ)

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5150052
Minimum0
Maximum83
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:21.887938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum83
Range83
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7613019
Coefficient of variation (CV)0.83306702
Kurtosis62.989402
Mean4.5150052
Median Absolute Deviation (MAD)2
Skewness4.9721286
Sum21514
Variance14.147392
MonotonicityNot monotonic
2023-05-24T05:13:22.062935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 951
20.0%
2 827
17.4%
4 685
14.4%
1 534
11.2%
5 513
10.8%
6 372
 
7.8%
7 244
 
5.1%
8 171
 
3.6%
9 114
 
2.4%
10 93
 
2.0%
Other values (24) 261
 
5.5%
ValueCountFrequency (%)
0 7
 
0.1%
1 534
11.2%
2 827
17.4%
3 951
20.0%
4 685
14.4%
5 513
10.8%
6 372
 
7.8%
7 244
 
5.1%
8 171
 
3.6%
9 114
 
2.4%
ValueCountFrequency (%)
83 1
 
< 0.1%
61 1
 
< 0.1%
49 1
 
< 0.1%
43 1
 
< 0.1%
41 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
27 1
 
< 0.1%
25 4
0.1%

all_fatalities
Real number (ℝ)

Distinct199
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.481847
Minimum0
Maximum583
Zeros74
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:22.229294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q325
95-th percentile87
Maximum583
Range583
Interquartile range (IQR)21

Descriptive statistics

Standard deviation35.676795
Coefficient of variation (CV)1.5869157
Kurtosis35.623846
Mean22.481847
Median Absolute Deviation (MAD)8
Skewness4.5569371
Sum107126
Variance1272.8337
MonotonicityNot monotonic
2023-05-24T05:13:22.417542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 367
 
7.7%
2 367
 
7.7%
3 359
 
7.5%
4 239
 
5.0%
5 232
 
4.9%
6 168
 
3.5%
7 157
 
3.3%
10 150
 
3.1%
8 125
 
2.6%
13 124
 
2.6%
Other values (189) 2477
52.0%
ValueCountFrequency (%)
0 74
 
1.6%
1 367
7.7%
2 367
7.7%
3 359
7.5%
4 239
5.0%
5 232
4.9%
6 168
3.5%
7 157
3.3%
8 125
 
2.6%
9 120
 
2.5%
ValueCountFrequency (%)
583 1
< 0.1%
520 1
< 0.1%
349 1
< 0.1%
346 1
< 0.1%
329 1
< 0.1%
301 1
< 0.1%
298 1
< 0.1%
290 1
< 0.1%
275 1
< 0.1%
271 1
< 0.1%

passenger_fatalities
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct190
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.967051
Minimum0
Maximum560
Zeros1039
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:22.594819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q321
95-th percentile81
Maximum560
Range560
Interquartile range (IQR)20

Descriptive statistics

Standard deviation34.087024
Coefficient of variation (CV)1.7971705
Kurtosis36.901251
Mean18.967051
Median Absolute Deviation (MAD)8
Skewness4.642977
Sum90378
Variance1161.9252
MonotonicityNot monotonic
2023-05-24T05:13:22.753450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1039
21.8%
1 304
 
6.4%
2 262
 
5.5%
3 192
 
4.0%
4 185
 
3.9%
5 139
 
2.9%
6 133
 
2.8%
8 126
 
2.6%
7 126
 
2.6%
9 118
 
2.5%
Other values (180) 2141
44.9%
ValueCountFrequency (%)
0 1039
21.8%
1 304
 
6.4%
2 262
 
5.5%
3 192
 
4.0%
4 185
 
3.9%
5 139
 
2.9%
6 133
 
2.8%
7 126
 
2.6%
8 126
 
2.6%
9 118
 
2.5%
ValueCountFrequency (%)
560 1
< 0.1%
505 1
< 0.1%
335 1
< 0.1%
316 1
< 0.1%
307 1
< 0.1%
287 1
< 0.1%
283 1
< 0.1%
278 1
< 0.1%
258 1
< 0.1%
257 1
< 0.1%

crew_fatalities
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5867786
Minimum0
Maximum43
Zeros398
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:22.914115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1747775
Coefficient of variation (CV)0.88513339
Kurtosis12.925451
Mean3.5867786
Median Absolute Deviation (MAD)2
Skewness2.5035007
Sum17091
Variance10.079212
MonotonicityNot monotonic
2023-05-24T05:13:23.065213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 892
18.7%
3 823
17.3%
1 769
16.1%
4 590
12.4%
5 402
8.4%
0 398
8.4%
6 273
 
5.7%
7 171
 
3.6%
8 130
 
2.7%
9 86
 
1.8%
Other values (18) 231
 
4.8%
ValueCountFrequency (%)
0 398
8.4%
1 769
16.1%
2 892
18.7%
3 823
17.3%
4 590
12.4%
5 402
8.4%
6 273
 
5.7%
7 171
 
3.6%
8 130
 
2.7%
9 86
 
1.8%
ValueCountFrequency (%)
43 1
 
< 0.1%
33 1
 
< 0.1%
27 1
 
< 0.1%
25 2
 
< 0.1%
23 6
0.1%
22 5
0.1%
21 2
 
< 0.1%
20 3
0.1%
19 5
0.1%
18 3
0.1%

ground
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size74.5 KiB

decade
Real number (ℝ)

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.6065
Minimum1900
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.8 KiB
2023-05-24T05:13:23.200306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11950
median1970
Q31990
95-th percentile2010
Maximum2020
Range120
Interquartile range (IQR)40

Descriptive statistics

Standard deviation24.749065
Coefficient of variation (CV)0.012584655
Kurtosis-0.94767328
Mean1966.6065
Median Absolute Deviation (MAD)20
Skewness-0.03293982
Sum9370880
Variance612.5162
MonotonicityIncreasing
2023-05-24T05:13:23.333364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1960 629
13.2%
1950 622
13.1%
1990 604
12.7%
1970 597
12.5%
1940 529
11.1%
1980 510
10.7%
2000 498
10.5%
1930 345
7.2%
2010 227
 
4.8%
1920 175
 
3.7%
Other values (3) 29
 
0.6%
ValueCountFrequency (%)
1900 2
 
< 0.1%
1910 13
 
0.3%
1920 175
 
3.7%
1930 345
7.2%
1940 529
11.1%
1950 622
13.1%
1960 629
13.2%
1970 597
12.5%
1980 510
10.7%
1990 604
12.7%
ValueCountFrequency (%)
2020 14
 
0.3%
2010 227
 
4.8%
2000 498
10.5%
1990 604
12.7%
1980 510
10.7%
1970 597
12.5%
1960 629
13.2%
1950 622
13.1%
1940 529
11.1%
1930 345
7.2%

year
Real number (ℝ)

Distinct110
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2109
Minimum1908
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.5 KiB
2023-05-24T05:13:23.516581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1908
5-th percentile1931
Q11951
median1970
Q31992
95-th percentile2010
Maximum2021
Range113
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.533143
Coefficient of variation (CV)0.012445722
Kurtosis-0.96211932
Mean1971.2109
Median Absolute Deviation (MAD)20
Skewness-0.025550453
Sum9392820
Variance601.87511
MonotonicityIncreasing
2023-05-24T05:13:23.748576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1947 78
 
1.6%
1948 78
 
1.6%
1962 76
 
1.6%
1972 74
 
1.6%
1989 73
 
1.5%
1946 73
 
1.5%
1951 72
 
1.5%
1970 71
 
1.5%
1950 70
 
1.5%
1994 70
 
1.5%
Other values (100) 4030
84.6%
ValueCountFrequency (%)
1908 1
 
< 0.1%
1909 1
 
< 0.1%
1912 1
 
< 0.1%
1913 1
 
< 0.1%
1915 1
 
< 0.1%
1916 1
 
< 0.1%
1918 1
 
< 0.1%
1919 8
0.2%
1920 17
0.4%
1921 11
0.2%
ValueCountFrequency (%)
2021 6
 
0.1%
2020 8
 
0.2%
2019 13
0.3%
2018 19
0.4%
2017 15
0.3%
2016 22
0.5%
2015 18
0.4%
2014 22
0.5%
2013 25
0.5%
2012 24
0.5%

Interactions

2023-05-24T05:13:13.321423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:25.284509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:31.091582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:42.026602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:46.735867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:55.333181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:03.801508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:08.508495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:13.468420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:25.515502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:32.044508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:42.233890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:47.380035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:55.952713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:03.987502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:08.662355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:14.900934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:27.013489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:34.128632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:43.551026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:49.323522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:57.637513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:05.252967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:10.170210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:15.167489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:27.298013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:35.141130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:43.866882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:50.092406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:58.654032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:05.556015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:10.429047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:16.444615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:28.931484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:37.733722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:44.974486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:51.589006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:00.152472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:06.693419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:11.496439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:18.198436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:30.387189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:39.435719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:46.051026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:53.059037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:01.622047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:07.858429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:12.682438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:18.433843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:30.644945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:40.273283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:46.335183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:54.037611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:02.316016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:08.135541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:12.999470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:18.594443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:30.808048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:41.026736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:46.539044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:12:54.683024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:03.185497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:08.328114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T05:13:13.168466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-24T05:13:23.948579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
all_aboardpassenger_aboardcrew_aboardall_fatalitiespassenger_fatalitiescrew_fatalitiesdecadeyear
all_aboard1.0000.4410.4830.0830.4160.2830.1720.171
passenger_aboard0.4411.0000.1230.2990.7840.0380.0750.073
crew_aboard0.4830.1231.0000.1660.1110.6790.0620.061
all_fatalities0.0830.2990.1661.0000.4480.2790.0680.070
passenger_fatalities0.4160.7840.1110.4481.0000.1690.0720.068
crew_fatalities0.2830.0380.6790.2790.1691.0000.0230.021
decade0.1720.0750.0620.0680.0720.0231.0000.994
year0.1710.0730.0610.0700.0680.0210.9941.000

Missing values

2023-05-24T05:13:18.993446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T05:13:19.459440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datetimelocationoperatorac_typeall_aboardpassenger_aboardcrew_aboardall_fatalitiespassenger_fatalitiescrew_fatalitiesgrounddecadeyear
01908-09-17Fort Myer, VirginiaMilitary - U.S. ArmyWright Flyer III211110019001908
11909-09-07Juvisy-sur-Orge, France?Wright Byplane101100019001909
21912-07-12Atlantic City, New JerseyMilitary - U.S. NavyDirigible505505019101912
31913-08-06Victoria, British Columbia, CanadaPrivateCurtiss seaplane101101019101913
61915-03-05Tienen, BelgiumMilitary - German NavyZeppelin L-8 (airship)4104117017019101915
101916-10-01Potters Bar, EnglandMilitary - German NavyZeppelin L-31 (airship)1901919019019101916
231918-12-16Elizabeth, New JerseyUS Aerial Mail ServiceDe Havilland DH-4101101019101918
241919-05-25Cleveland, OhioUS Aerial Mail ServiceDe Havilland DH-4101101019101919
251919-07-19Dix Run, PennsylvaniaUS Aerial Mail ServiceDe Havilland DH-4101101019101919
271919-08-02Verona, ItalyCaproni CompanyCaproni Ca.481412214122019101919
datetimelocationoperatorac_typeall_aboardpassenger_aboardcrew_aboardall_fatalitiespassenger_fatalitiescrew_fatalitiesgrounddecadeyear
49972020-05-22Karachi, PakistanPakistan International AirlineAirbus A320-2149991897898120202020
49982020-08-07Calicut, IndiaAir India ExppressBoeing 737-8HG190184620182020202020
49992020-08-22Juba, South SudanSouth West AviaitonAntonov 26B853743020202020
50002020-09-25Near Chuguev, UkraineMilitary - Ukraine Air ForceAntonov An26SH2720726197020202020
50012021-01-09Near Jakarta, IndonesiaSriwijaya AirBoeing 737-5246256662566020202021
50022021-03-02Pieri, SudanSouth Sudan Supreme AirlinesLet L-410UVP-E10821082020202021
50032021-03-28Near Butte, AlaskaSoloy HelicoptersEurocopter AS350B3 Ecureuil651541020202021
50042021-05-21Near Kaduna, NigeriaMilitary - Nigerian Air ForceBeechcraft B300 King Air 350i11741174020202021
50052021-06-10Near Pyin Oo Lwin, MyanmarMilitary - Myanmar Air ForceBeechcraft 1900D1412212111020202021
50072021-07-06Palana, RussiaKamchatka Aviation EnterpriseAntonov An 26B-1002822628226020202021